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Low prevalence of autoimmune diabetes markers in a mixed ethnic population of Singaporean diabetics

Todd AL, Ng WY, Lui KF, Thai AC

Intern Med J, 2004 Jan-Feb;34(1-2):24-30.
PMID: 14748910 DOI: 10.1111/j.1444-0903.2004.00482.x

BACKGROUND: Circulating antibodies to glutamic acid decarboxylase (GADab) and tyrosine phosphatase-like molecule IA-2 (IA-2ab) are major indicators for auto-immune destruction of pancreatic islet cells. They identify a majority of Caucasians with type 1 diabetes and approximately 50% of Asians, providing evidence of an idiopathic aetiology in the latter. The present study investigated these autoantibodies in a mixed ethnic group.
METHODS: Hospital clinic patients with clinically defined type 1 (n = 93) and type 2 (n = 300) diabetes and representing Singapore's major ethnic groups--Chinese, Indians and Malays--were studied. GADab and IA-2ab frequencies, and association of autoimmunity status with clinical and biochemical profiles were analysed.
RESULTS: Radio-immunoprecipitation assays detected either or both antibodies (seropositivity) in 41.9% of subjects with type 1 diabetes. GADab was detected in 36.6% and IA-2ab in 23.7% of type 1 diabetics. Prevalence of IA-2ab showed a reduction in frequency with disease duration (P = 0.026). In clinical type 2 diabetics, seropositivity was 10.0% with higher frequency in Malays (17.5%) than Chinese (9.7%) and Indians (4.5%). Multivariate analysis revealed that low fasting C-peptide was associated with seropositivity (odds ratio (OR) = 0.15; 95% confidence interval (CI) = 0.04-0.58). A significant relationship (OR = 13.5; 95% CI = 5.0-36.7) between insulin requirement and duration (>5 years) was also revealed. In patients with type 2 diabetes there was a trend of gradual progression to insulin dependency. However, there was considerable variation in body mass index between ethnic subgroups of type 2 diabetics, particularly for Chinese (mean (SD) = 26.0 (4.7)) and Malays (mean (SD) = 29.2 (5.9); P < 0.001).
CONCLUSIONS: Presence of both antibodies in our mixed ethnic group of type 1 diabetes patients was much lower than in Caucasians. Significant numbers of patients were seronegative for antibodies. Influences due to ethnicity and adiposity would require further investigations.
Fulltext Ranking negative emissions technologies under uncertainty

Ng WY, Low CX, Putra ZA, Aviso KB, Promentilla MAB, Tan RR

Heliyon, 2020 Dec;6(12):e05730.
PMID: 33364497 DOI: 10.1016/j.heliyon.2020.e05730

Existing mitigation strategies to reduce greenhouse gas (GHG) emissions are inadequate to reach the target emission reductions set in the Paris Agreement. Hence, the deployment of negative emission technologies (NETs) is imperative. Given that there are multiple available NETs that need to be evaluated based on multiple criteria, there is a need for a systematic method for ranking and prioritizing them. Furthermore, the uncertainty in estimating the techno-economic performance levels of NETs is a major challenge. In this work, an integrated model of fuzzy analytical hierarchy process (AHP) and interval-extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to address the multiple criteria, together with data uncertainties. The potential of NETs is assessed through the application of this hybrid decision model. Sensitivity analysis is also conducted to evaluate the robustness of the ranking generated. The result shows Bioenergy with Carbon Capture and Storage (BECCS) as the most optimal alternative for achieving negative emission goals since it performed robustly in the different criteria considered. Meanwhile, energy requirement emerged as the most preferred or critical criterion in the deployment of NETs based on the decision-maker. This paper renders a new research perspective for evaluating the viability of NETs and extends the domains of the fuzzy AHP and interval-extended TOPSIS hybrid model.
Fulltext Induction approach via P-Graph to rank clean technologies

Low CX, Ng WY, Putra ZA, Aviso KB, Promentilla MAB, Tan RR

Heliyon, 2020 Jan;6(1):e03083.
PMID: 31909259 DOI: 10.1016/j.heliyon.2019.e03083

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation.
Clinical manifestations, laboratory profile and outcomes of dengue virus infection in hospitalised older patients

Ng WY, Ngim CF, Chow KY, Goh SXM, Zaid M, Dhanoa A

Trans R Soc Trop Med Hyg, 2021 Nov 09.
PMID: 34750632 DOI: 10.1093/trstmh/trab168

BACKGROUND: Due to an ageing population, dengue among older patients is encountered more frequently in many countries. Our study aimed to explore the clinico-laboratory parameters and outcomes among dengue-infected older patients in comparison with younger patients.
METHODS: This retrospective chart review involved dengue patients with dengue non-structural protein 1 (NS1) antigen positivity admitted to a tertiary hospital in Malaysia from January to July 2015. A comparison was made between older people (aged ≥60 y) and others.
RESULTS: Among 406 dengue patients, 43 (10.6%) were older people. Older dengue patients were less likely to present with persistent vomiting (adjusted OR [AOR] 0.247, 95% CI 0.093 to 0.656, p=0.005), while restlessness and confusion were more common at presentation (AOR 3.356, 95% CI 1.024 to 11.003, p=0.046). Older patients had significantly lower albumin upon admission (38 vs 40 g/L, p=0.036) and during hospital stay (35 vs 37 g/L, p=0.015). Compared with younger patients, older patients were more likely to have experienced nadir platelet counts of <50×109/L (AOR 2.897, 95% CI to 1.176 to 7.137, p=0.021). They were also more likely to require an extended hospital stay (AOR 3.547, 95% CI 1.575 to 7.986, p=0.002).
CONCLUSIONS: Diagnosis of dengue in older people may be challenging because of atypical presentations. Increased vigilance is necessary as there is an increased tendency to develop severe thrombocytopenia, hypoalbuminemia and prolonged hospitalisation in older people.
Changing prevalence of diabetes mellitus in Singapore over a ten year period

Thai AC, Yeo PP, Lun KC, Hughes K, Wang KW, Sothy SP, et al.

J Med Assoc Thai, 1987 Mar;70 Suppl 2:63-7.
PMID: 3598446
Fulltext Asperuloside Enhances Taste Perception and Prevents Weight Gain in High-Fat Fed Mice

Ishaq M, Tran D, Wu Y, Nowak K, Deans BJ, Xin JTZ, et al.

Front Endocrinol (Lausanne), 2021;12:615446.
PMID: 33927690 DOI: 10.3389/fendo.2021.615446

Asperuloside is an iridoid glycoside found in many medicinal plants that has produced promising anti-obesity results in animal models. In previous studies, three months of asperuloside administration reduced food intake, body weight, and adipose masses in rats consuming a high fat diet (HFD). However, the mechanisms by which asperuloside exerts its anti-obesity properties were not clarified. Here, we investigated homeostatic and nutrient-sensing mechanisms regulating food intake in mice consuming HFD. We confirmed the anti-obesity properties of asperuloside and, importantly, we identified some mechanisms that could be responsible for its therapeutic effect. Asperuloside reduced body weight and food intake in mice consuming HFD by 10.5 and 12.8% respectively, with no effect on mice eating a standard chow diet. Fasting glucose and plasma insulin were also significantly reduced. Mechanistically, asperuloside significantly reduced hypothalamic mRNA ghrelin, leptin, and pro-opiomelanocortin in mice consuming HFD. The expression of fat lingual receptors (CD36, FFAR1-4), CB1R and sweet lingual receptors (TAS1R2-3) was increased almost 2-fold by the administration of asperuloside. Our findings suggest that asperuloside might exert its therapeutic effects by altering nutrient-sensing receptors in the oral cavity as well as hypothalamic receptors involved in food intake when mice are exposed to obesogenic diets. This signaling pathway is known to influence the subtle hypothalamic equilibrium between energy homeostasis and reward-induced overeating responses. The present pre-clinical study demonstrated that targeting the gustatory system through asperuloside administration could represent a promising and effective new anti-obesity strategy.
Fulltext Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Gunasekeran DV, Zheng F, Lim GYS, Chong CCY, Zhang S, Ng WY, et al.

Front Med (Lausanne), 2022;9:875242.
PMID: 36314006 DOI: 10.3389/fmed.2022.875242

BACKGROUND: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.
METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.
RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.
CONCLUSION: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.